Digital twin management system and method

ABSTRACT

A digital twin management system manages a virtual model that represents an actual physical system in a virtual space on a real-time basis. To generate an integrated virtual model by adding a second virtual model to a first virtual model, a processor of the digital twin system extracts multiple parts that can be used in common in the first virtual model and the second virtual model, generates multiple integrated virtual models that are candidates of an integrated virtual model by changing the extracted parts that can be used in common, calculates an evaluation of each of the generated integrated virtual models, and outputs configuration information regarding each of the integrated virtual model candidates and an evaluation of the integrated virtual model candidate in association with each other.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a digital twin management system and adigital twin management method.

2. Description of the Related Art

A cyber physical system (CPS) that uses a digital twin in a cyber(digital) world to represent an apparatus, a process, a plant, or asystem in the real world in order to understand, predict, or optimize aphenomenon has been studied. Through a digital twin, a behavior of aphysical apparatus or the like can be predicted or verified by asimulation in a digital space or any other analysis method. In order tosimplify the configuration of a digital twin, a method for implementingan efficient configuration according to a target apparatus or the likeis proposed (U.S. Pat. No. 10,274,915 and US Patent ApplicationPublication No. 2014/0019104).

Aside from a digital twin, a technology for, in a case where a pluralityof patterns of processes that are different only in parameters from oneanother are given, executing the processes while omitting overlappingportions in order to enhance the efficiency of machine learningprocesses has been known (JP-2012-160014-A).

In a digital twin, when a simulation of a behavior of a physicalapparatus or the like in a digital space or any other analysis method isused to predict performance or determine/verify a failure, an additionalanalysis method is applied or information regarding an additionalsensor, etc., is added to, for example, the simulation model or theanalysis model (hereinafter, referred to as a model) in some cases, sothat addition or update of a model is conducted.

For example, in a digital twin environment (sensor, configuration/datamanagement, model) for estimating the remaining life of a motor of amachining apparatus, an additional function for estimating the degree ofabrasion of a drill which is a consumable item in the machiningapparatus is added.

In this case, it takes time for a user who is a system manger or thelike to determine whether or not the addition or update of the functionis suited to the existing digital twin environment, and to recognize, ineach site, a range to be changed or affected by the addition or update.

The conventional technologies disclose a method for dynamicallyconfiguring a new digital twin, but none of these technologies disclosea configuration method in which a possible effect on an existing digitaltwin environment is taken into consideration. Therefore, theuser-friendliness is poor.

The present invention has been achieved in view of the abovementionedproblems, and an object thereof is to provide a digital twin managementsystem and a digital twin management method having improveduser-friendliness.

SUMMARY OF THE INVENTION

In order to solve the above problems, a digital twin management systemaccording to one aspect of the present invention for managing a digitaltwin that represents an actual physical system in a virtual space, thedigital twin management system includes a processor and a memory thatstores a predetermined computer program that is executed by theprocessor. In the digital twin management system, to generate anintegrated scenario by adding a second scenario to a first scenarioforming a digital twin, the processor extracts multiple parts that canbe used in common in the first scenario and the second scenario,generates multiple integrated scenario candidates that are candidates ofthe integrated scenario, by changing the extracted parts that can beused in common, calculates an evaluation of each of the integratedscenario candidates, and outputs configuration information regardingeach of the integrated scenario candidates and the evaluation of theintegrated scenario candidate in association with each other.

According to the present invention, the configuration information andevaluation regarding each integrated scenario candidate formed by addingthe second scenario to the first scenario forming the digital twin canbe outputted, so that, in reference to the outputted configurationinformation and evaluation, a user can determine a possible effect byupdate of the digital twin. Accordingly, the user-friendliness isimproved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a digital twin management unitaccording to the present embodiment;

FIG. 2 is a configuration diagram of a digital twin management systemand an Internet of Things (IoT) management system;

FIG. 3 is a hardware configuration diagram of a computer that is used inthe digital twin management system;

FIG. 4 illustrates an example of an apparatus management database;

FIG. 5 illustrates an example of a digital twin configuration database;

FIG. 6 illustrates an example of an analysis model database;

FIG. 7 illustrates an example of a grouping policy database;

FIG. 8 illustrates an example of a data store management database;

FIG. 9 illustrates an example of a scenario template database;

FIG. 10 illustrates an example of a scenario application plan database;

FIG. 11 illustrates an example of a scenario instance database;

FIG. 12 is a flowchart of a digital twin management process;

FIG. 13 is a template process flow which is included in a remainingmotor life estimation scenario;

FIG. 14 is a templated process flow which is included in a remainingdrill life estimation scenario;

FIG. 15 is an explanatory diagram illustrating one example of anintegrated scenario candidate (updated digital twin candidate);

FIG. 16 is an explanatory diagram illustrating another example of theintegrated scenario candidate;

FIG. 17 is an example of a screen through which information regardingintegrated scenario candidates is provided to a user;

FIG. 18 is an example illustrating a part of the screen in FIG. 17 indetail; and

FIG. 19 is a flowchart of a digital twin management process according toa second embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, embodiments of the present invention will be explained inreference to the drawings. A digital twin management system according toany one of these embodiments has, as scenario templates, configurations,data sets, data processing, behavior models, failure models, and processflows in a system for predicting or analyzing a remaining life or afailure.

The digital twin management system according to any one of theembodiments can be regarded as a system for managing a digital twin. Adigital twin is a technology of collecting the operation status of anapparatus or device that is an actual physical system from a sensor, andcreating a model in a virtual space in reference to the collectedinformation. A digital twin created in a virtual space is linked with anactual system. For example, this technology can be used to calculate aremaining life, diagnose a failure, determine a trouble, and analyzeperformance.

In the digital twin management system according to any one of theembodiments, when a new scenario template is added to a digital twinenvironment being executed, to create an integrated scenario, thescenario template is compared with the configuration, data, dataprocessing, a behavior model, a failure model, and an analysis flow inthe existing environment. As a result, the digital twin managementsystem creates multiple candidates the configuration of which can bepartially used in common, calculates an impact (evaluation) to beexerted by each of the candidates, and presents the calculation resultto a user.

Thus, according to any one of the embodiments, a user can objectivelyselect a candidate that is determined as an optimum one, in reference tothe information and the evaluations regarding the respective candidates,so that the user-friendliness is improved. Furthermore, according to anyone of the embodiments, even a less-experienced user can properly managea digital twin.

First Embodiment

The first embodiment will be explained with reference to FIGS. 1 to 18 .FIG. 1 is a schematic diagram of a digital twin management unit 120which is one of elements constituting a digital twin management system100.

The digital twin management unit 120 includes a scenario addition unit10, a common-point extraction unit 11, a candidate generation unit 12,an evaluation unit 13, a narrowing-down unit 14, and an informationpresentation unit 15, for example. The digital twin management unit 120may further include a configuration change unit 16. In addition, thedigital twin management unit 120 may further include a systemnotification unit 17.

In the digital twin management unit 120, when the scenario addition unit10 adds a new scenario to an existing scenario, the common-pointextraction unit 11 compares a process flow of the existing scenario witha process flow of the new scenario, and extracts a part that can be usedin common.

By changing a part that can be used in common, the candidate generationunit 12 generates multiple candidates of an integrated scenario(referred to as an integrated scenario or an updated scenario).

The evaluation unit 13 evaluates each of the integrated scenariocandidates according to a predetermined evaluation index. Examples ofthe predetermined evaluation index include a usage rate of computerresources and performance stability; indexes which are related directlyto the integrated scenario candidates, and are elements constituting adigital twin.

Besides these evaluation indexes or in place of these evaluationindexes, an index that has an effect on a separate system linked with anactual system (e.g., industrial apparatus) may be used. Examples of theseparate system linked with an actual system include a productionmanagement system, a maintenance management system, and an inventorymanagement system. Examples of the effect to be exerted on the separatesystem by update of a digital twin linked with an actual system includea change in the number of products that are produced by the actualsystem, a change in the inventory number of maintenance parts requiredfor maintaining the actual system, and a change in a maintenance planfor the actual system.

The narrowing-down unit 14 selects, from among the multiple integratedscenario candidates generated by the candidate generation unit 12, oneor more candidates to be presented to a user, in reference to theevaluations calculated by the evaluation unit 13. The narrowing-downunit 14 may group the integrated scenario candidates in accordance witha preset grouping policy, and select a candidate having the highestevaluation in a candidate group formed by the grouping. For example, themultiple integrated scenario candidates may be grouped in accordancewith such a policy as a resource sharing rate, a change rate of a modelaccuracy, and the amount of change in a process time.

The information presentation unit 15 presents the integrated scenariocandidate selected by the narrowing-down unit 14 to the user through ascreen G1 being displayed on a monitor display. The informationpresentation screen G1 includes a candidate display section GP11 and anevaluation display section GP12, for example. As illustrated in FIG. 17, which will be explained later, the screen G1 may include a region fordisplaying a general description of an update.

In the candidate display section GP11, a general description of one ormore integrated scenario candidates is displayed. The generaldescription includes a process flow of a first scenario, a process flowof a second scenario, and a common process flow (process flow of acommon layer) which is used in both the first scenario and the secondscenario, for example. Each process flow is not necessarily required tobe displayed in detail as long as it is displayed to such an extent thatthe user can understand the general description. Further, a button forindicating the details may be disposed on the screen G1, so that thedetails of each process flow are displayed when the button is operatedby the user. The first scenario refers to an addition destinationscenario to which another scenario (second scenario) is to be added. Thesecond scenario is a to-be-added scenario which is to be added toanother scenario (first scenario). In the present embodiment, a casewhere one second scenario is added to one first scenario is explained.However, the present embodiment is not limited to this case, and is alsoapplicable to a case where multiple second scenarios are added to onefirst scenario.

In the evaluation display section GP12, the evaluations based on apredetermined evaluation index are displayed for the respectiveintegrated scenario candidates. For example, names for identifying therespective integrated scenario candidates and evaluation values (e.g., apossible effect on the performance, the resource sharing rate, a lengthof time required for update) are displayed in the evaluation displaysection GP12.

The configuration change unit 16 updates, when the user selects one ofthe integrated scenario candidates, an existing digital twin to theselected integrated scenario candidate.

The system notification unit 17 transmits a predetermined notificationto an actual system (e.g., industrial apparatus) and/or a separatesystem linked with the actual system when the update to an integratedscenario candidate is conducted. The predetermined notification isinformation indicating a possible effect on the actual system orinformation indicating a possible effect on the separate system linkedwith the actual system, for example.

With reference to FIG. 2 , the overall configuration of an IoTmanagement system 1 according to the present embodiment will beexplained. The IoT management system 1 includes the digital twinmanagement system 100, a digital twin process execution unit 200,industrial apparatuses 300, a manufacturing execution system (MES) 410,and an IoT gateway 420, for example. The digital twin management system100 and the digital twin process execution unit 200 can be formed ofcomputers CS which will be explained later with reference to FIG. 3 .

The digital twin management system 100 manages the industrialapparatuses 300 in a factory and the statuses of components included inthe industrial apparatuses 300. The details of the digital twinmanagement system 100 will be explained later.

The digital twin process execution unit 200 executes processing of adigital twin. The digital twin process execution unit 200 includes adata collection/processing unit 210, a scenario execution unit 220, anda data store 230, for example. Hereinafter, the datacollection/processing unit 210 may be abbreviated as a data collectionunit 210.

The data collection unit 210 obtains data from a sensor 330 of eachindustrial apparatus 300. The data obtained from the sensors 330 isstored in the data store 230. The scenario execution unit 220 executes apredetermined process of estimating a remaining life or detecting anabnormality, for example, in reference to the data obtained from thesensors 330 and a process flow.

In the abovementioned manner, the digital twin process execution unit200 receives data from the industrial apparatuses 300 in the factory,analyzes the data, and outputs the analysis result. When the quantityand length of time of communication between the digital twin processexecution unit 200 and the industrial apparatuses 300 are taken intoconsideration, it is preferable that the physical distance between thedigital twin process execution unit 200 and each industrial apparatus300 be as short as possible. As such, the digital twin process executionunit 200 of the present embodiment is installed in the factory where theindustrial apparatuses 300 are disposed, or in a building that is closeto the factory.

Placing the digital twin process execution unit 200 close to theindustrial apparatuses 300 brings about the abovementioned advantage,but the present embodiment is not limited to this case. The digital twinprocess execution unit 200 may be placed at a physically great distancefrom each industrial apparatus 300. The digital twin process executionunit 200 may be placed in a computer in which the digital twinmanagement system 100 is also included.

Each industrial apparatus 300 is one example of the “actual system,” andis used in the factory. Examples of the industrial apparatuses 300include cutting machines, pressing machines, rolling machines, injectionmolding machines, carrier devices, drying furnaces, heating furnaces,reaction furnaces, stirring devices, centrifugal machines, and packagingmachines. In the present embodiment, cutting machines will be describedas one example of the industrial apparatus 300.

It is to be noted that the actual systems are not limited to industrialapparatuses. For example, a man-machine conveyor device such as anelevator or escalator or such an apparatus as an automatic door or airblower that is used in an office, a commercial facility, or a publicfacility may be used as the actual system.

Each industrial apparatus 300 of the present embodiment includes a motor310, a drill 320, the sensor 330, a power source 340, and anunillustrated controller, for example. Upon receiving power from thepower source 340 serving as a “motive power source,” the motor 310serving as a “driving source” is rotated to rotate the drill 320 servingas a “consumable item.” Accordingly, an object to be machined is cut,for example.

The sensor 330 detects the rotational speed of the motor 310, thecurrent in the motor 310, the axial voltage in the motor 310, and thetorque of the motor 310, for example, and outputs digital data. It isnot necessary to use, as the sensor 330 included in each industrialapparatus 300, an actual sensor that actually measures a physicalquantity of an object to be measured. At least one of the sensors 330may be a virtual sensor that outputs data according to measurement dataobtained by an actual sensor. When measurement data obtained by theactual sensor is inputted to a predetermined logic, output of thevirtual sensor is obtained. The predetermined logic refers to a logic(analysis model) used for estimating a behavior of a target. Through thepredetermined logic, the rotational speed or torque of the motor 310 isestimated according to an actual current value in the motor 310. Thedata obtained from the actual sensor and the data obtained from thevirtual sensor are examples of the “data derived from the actualsystem.”

The MES 410 is a production execution system. The MES 410 is connectedto the industrial apparatuses 300 in a communicable manner. The MES 410recognizes the respective statuses of the industrial apparatuses 300,and manages production steps. The MES 410 gives an instruction to aworker, and supports the work being conducted by the worker.

The IoT gateway 420 collects data from the sensors 330 of the respectiveindustrial apparatuses 300, and transmits the data to the digital twinprocess execution unit 200.

The separate system 500 is related to the industrial apparatuses 300which are actual systems. Examples of the separate system 500 include adesign support system, a component management system, an inventorymanagement system, a purchase system, a maintenance management system,and a worker management system.

The digital twin management system 100 is capable of providing data tothe separate system 500, according to respective analysis resultsregarding the industrial apparatuses 300. For example, when there is achange in the remaining life of a certain industrial apparatus 300 or acomponent thereof, a maintenance plan needs to be changed, the number ofcomponents to be ordered needs to be adjusted, and an inventory needs tobe adjusted. As such, the separate system 500 executes a necessaryprocess in reference to information provided from the digital twinmanagement system 100.

The digital twin management unit 120 manages a digital twinconfiguration DB T2, an analysis model DB T3, a grouping policy DB T4, ascenario application plan DB T7, and a scenario instance DB T8. Thedigital twin management unit 120 executes the process previouslyexplained with reference to FIG. 1 and processes which will be explainedlater with reference to FIGS. 12 and 19 . A data store management unit140 manages, in the data store management DB T5, meta-informationregarding data collected from the actual sensors and virtual sensors ofthe industrial apparatuses 300. A scenario template management unit 150manages the scenario template DB T6. A user interface 160 for managementimplements a function of displaying a management screen.

FIG. 3 illustrates a hardware configuration example of a computer CSwhich can be used as the digital twin management system 100 or thedigital twin process execution unit 200.

The computer CS includes a processor 101, a memory 102, an auxiliarystorage device 103, a communication device 104, an output device 105, aninput device 106, and a reading-and-writing device 107, for example. Thedevices 101 to 107 are connected via a bus 108.

The processor 101 reads a predetermined computer program transferredfrom the auxiliary storage device 103 to the memory 102, and executesthe read program, whereby a predetermined function is implemented. In acase where the computer CS is used as the digital twin management system100, the predetermined function is a function of the digital twinmanagement system 100.

The communication device 104 communicates with other computers 200, 500,etc., over a communication network CN. The communication network CN maybe a public line or may be a dedicated line.

The output device 105 provides information to a user of the digital twinmanagement system 100 who is, for example, a system manager. Forexample, the output device 105 is a monitor display, a printer, or aspeech synthesis device.

The input device 106 obtains an instruction or information from theuser. For example, the input device 106 is a keyboard, a touch panel, apointing device, or a speech recognition device.

The reading-and-writing device 107 writes/reads data into/from anexternal memory medium MM. For example, the external memory medium MM issuch a medium as a flash memory, a hard disk, an optical disk, or amagnetic tape, in which stored data can be held for a long period oftime.

A computer program or data stored in the external memory medium MM canbe transferred from the reading-and-writing device 107 to the memory 102or the auxiliary storage device 103. Further, a computer program or datastored in the memory 102 or the auxiliary storage device 103 can betransferred to the external memory medium MM. A portion or the whole ofa predetermined computer program for implementing each function in thedigital twin management system 100 of the present embodiment may bestored in the external memory medium MM, and the external memory mediumMM may be sold on the market.

Alternatively, a predetermined computer program regarding the digitaltwin management system 100 may be transferred to a separate computer ora separate storage device (which are not illustrated) over thecommunication network CN without using any external memory medium MM. Inaddition, the digital twin management system 100 may receive a computerprogram or data from an external program distribution server (notillustrated), and may store the computer program or data in theauxiliary storage device 103.

The output device 105 and the input device 106, which are included inthe computer CS and are used for information exchange with a user, maybe disposed in an operation terminal (not illustrated) that is separatefrom the computer CS. For example, a desktop type personal computer, alaptop type personal computer, a tablet terminal, a cellular phone (orwhat is generally called a smartphone), a mobile information terminal,or a wearable terminal may be used as the operation terminal.

FIG. 4 illustrates an example of the apparatus management database T1.In the apparatus management database T1, information regardingapparatuses (the industrial apparatuses 300) that are being managed bythe digital twin management system 100 is managed.

The apparatus management database T1 includes an asset ID C11, anapparatus name C12, a model C13, configuration information C14, aninstallation site C15, and an installation date C16, for example.

The asset ID C11 is identification information for uniquely identifyingeach industrial apparatus 300 that is being managed by the digital twinmanagement system 100. The apparatus name C12 is a name of thecorresponding industrial apparatus 300, and is information indicatingthe apparatus type which is a “cutting machine 001,” a “pressing machine007,” or an “injection molding machine 333,” for example.

The model C13 indicates information for identifying models of theindustrial apparatuses 300 of the same type. Some of the industrialapparatuses 300 of the same type have slightly different specifications,and thus, different model names are given therefor.

The configuration information C14 is information indicating theconfiguration of the corresponding industrial apparatus 300. Forexample, the information indicating the configuration of thecorresponding industrial apparatus 300 refers to the number of maincomponents constituting the corresponding industrial apparatus 300 andthe model type of each of the main components. A list of main componentsconstituting each industrial apparatus 300 may be prepared, andinformation for identifying the list may be stored in the field of theconfiguration information C14.

The installation site C15 is information for identifying the site wherethe corresponding industrial apparatus 300 is installed. Theinstallation site C15 may be specified by a building number and a linenumber, for example. Values indicating coordinates in the site of thefactory may be stored in the installation site C15. The installationdate C16 indicates the date of installation the corresponding industrialapparatus 300 into the factory.

FIG. 5 illustrates an example of the digital twin configuration databaseT2. In the digital twin configuration database T2, the configuration ofa digital twin that represents an industrial apparatus 300 in a virtualspace is managed.

The digital twin configuration database T2 includes a digital twin ID(abbreviated as DTID in the drawings) C21, a name C22, a targetapparatus ID C23, meta-information C24, monitoring information C25, aconfigured scenario instance C26, and digital twin relationshipinformation C27, for example.

The digital twin ID (DTID in the drawing) C21 indicates identificationinformation for identifying a digital twin. The name C22 is the name ofthe digital twin. The name of the digital twin may include at least aportion of the name of an industrial apparatus corresponding to thedigital twin.

The target apparatus ID C23 indicates identification information foridentifying an industrial apparatus 300 corresponding to the digitaltwin. For the target apparatus ID C23, a value identical to that of theasset ID C11 in the apparatus management database T1 is used.

The meta-information C24 is meta-information regarding the digital twin.Examples of the meta-information include a model name, a serial number(S/N), and an installation date.

The monitoring information C25 is sensor data, regarding the associatedindustrial apparatus 300, being monitored by the digital twin. In orderto be linked with information being managed in the data store managementDB T5, DID C51 is stored in the monitoring information C25.

The sensor data is data obtained from a real sensor 330 (actual sensor330) or data obtained from a virtual sensor. The data from a virtualsensor is obtained when the data obtained from the actual sensor 330 isinputted to a predetermined logic (analysis model).

The configured scenario instance C26 indicates an instance of aconfigured scenario. According to the scenario, the remaining life of amotor 310 is estimated from a motor current, a motor axis voltage, and amotor rotational speed, for example. Any other type of a scenario may beprepared.

The digital twin relationship information C27 indicates informationregarding the relationship with another digital twin. For example, for acutting apparatus digital twin 001 (DTID: DT-001) which is a digitaltwin of a cutting apparatus 001, the relationship with an industrialmotor digital twin 001 (DTID: DT-003) of an inner industrial motor Awhich is one of components constituting the cutting apparatus 001 isindicated as “Child,” and this information is stored in the digital twinrelationship information C27. In addition, a relationship “Parent” withthe cutting apparatus digital twin 001 (DTID: DT-001) which is a digitaltwin of the cutting apparatus 001 is stored in the digital twinrelationship information C27 of the industrial motor digital twin 001.

FIG. 6 illustrates an example of the analysis model database T3. In theanalysis model database T3, an analysis model and the relation betweenthe analysis model and a scenario are managed.

The analysis model database T3 includes a serial number C31, a name C32,a model ID C33, a type C34, a scenario ID C35, an input parameter C36,an output parameter C37, and a setting parameter C38, for example.

The serial number C31 indicates a serial number that is assigned to eachrecord. The name C32 indicates an analysis model name. Each analysismodel name may be given to make the role (purpose), which is notindicated in FIG. 6 , of the corresponding analysis model recognizableby a user. The analysis model name is current leveling (NM1), axisvoltage estimation (NM2), torque estimation (NM3), rotational speedestimation (NM4), remaining motor bearing life estimation (NM5), currentleveling (NM6), torque estimation (NM7), rotational speed estimation(NM8), or cutting machine drill failure diagnosis (NM9), for example.

The model ID (MID in the drawings) C33 is identification information foruniquely identifying an analysis program constituting the correspondinganalysis model. Here, in FIG. 6 , M-03 is an identifier of a torqueestimation program, and M-03-ver.2 is an identifier of an updated torqueestimation program which is a different version of M-03 obtained byimproving M-03 in terms of the estimation accuracy, for example.Similarly, in FIG. 6 , M-04 is an identifier of a rotational speedestimation program, and M-04-ver.2 is an identifier of an updatedrotational speed estimation program which is a different version of M-04obtained by improving M-04. The type C34 indicates the type of thecorresponding analysis model. The analysis model type is pre-process(MT1), behavior estimation (MT2), or failure determination (MT3), forexample.

The scenario ID C35 indicates information for identifying a scenario inwhich the corresponding analysis model is used. The input parameter C36indicates a parameter that is inputted to the corresponding analysismodel. The input parameter is actual motor current (RS-01), motorcurrent (after application of a high-pass filter) (VS-01), axis voltage(VS-02), torque (VS-03), rotational speed (VS-04), motor current (afterapplication of a high-pass filter) (VS-06), material quality (M-01),usage history (M-02), torque (VS-07), or rotational speed (VS-08), forexample.

The output parameter C37 indicates a parameter that is outputted fromthe corresponding analysis model. The output parameter is motor current(after application of a high-pass filter) (VS-01), axis voltage (VS-02),torque (VS-03), rotational speed (VS-04), remaining life (VS-05), motorcurrent (after application of a high-pass filter) (VS-06), torque(VS-07), rotational speed (VS-08), or drill abrasion state (VS-09), forexample.

The setting parameter C38 indicates a parameter that is set for thecorresponding analysis model. The setting parameter C38 is a high-passfilter (HPF), for example.

FIG. 7 illustrates an example of the grouping policy database T4. Agrouping policy refers to a standard for grouping integrated scenariocandidates.

The grouping policy database T4 includes a policy ID (PID in thedrawing) C41, an index C42, a value C43, a priority level C44, and aselection policy C45, for example.

The policy ID C41 is information for identifying a grouping policy. Theindex C42 indicates the content of the corresponding grouping policy.Examples of the index include a resource sharing rate, an accuracychange rate, and a change of a processing time period. The value C43indicates the range of values used for grouping multiple integratedscenario candidates according to the index C42. The priority level C44indicates the priority level of the corresponding grouping policy. Theselection policy C45 indicates a criterion used to select an integratedscenario.

FIG. 8 illustrates an example of the data store management database T5.In the data store management database T5, data that is used in thedigital twin management system 100 is managed. The data store managementdatabase T5 includes a data store ID C51, a name C52, a type C53,meta-information C54, and a field C55, for example.

The data store ID (DID in the drawings) C51 indicates information foridentifying data being managed. The name C52 is the name of the databeing managed. The type C53 indicates the type of the data, that is,indicates whether the data is time-series data or other types of data.The meta-information C54 indicates attribute information regarding thedata being managed. The meta-information is information regarding fromwhat the corresponding data is derived (whether the data is obtainedfrom an actual sensor or a virtual sensor) and a collection interval ofthe data (if the data is derived from an actual sensor).

The field C55 indicates items included in the data being managed. In thedata field, for example, data acquisition date and time (time stamp),identification information (asset ID) regarding an industrial apparatuscorresponding to the data, identification information (DTID) of adigital twin corresponding to the industrial apparatus, and data values(current value, voltage value, torque, operation) are stored.

FIG. 9 illustrates an example of the scenario template database T6. Inthe scenario template database T6, templates of scenarios are managed.In the present embodiment, a predetermined information group that isrequired to add a model (simulation function) to a digital twin istemplated as a scenario. The scenario template database T6 includes ascenario ID (SID in the drawings) C61, a name C62, a target apparatusmodel C63, and prerequisite data C64, for example.

The scenario ID C61 indicates information for identifying a scenariotemplate. The name C62 is the name of the corresponding scenariotemplate. For example, a “remaining motor life estimation scenario” or a“remaining drill life estimation scenario” from which the role of thecorresponding template can be recognized can be used as the name. In thetarget apparatus model C63, an identifier for identifying an apparatusmodel to which the corresponding scenario is applicable is stored. Theprerequisite data C64 indicates a prerequisite for using thecorresponding scenario template. For example, in order to use theremaining motor life estimation scenario, a motor current is required.In order to use the remaining drill life estimation scenario, a motorcurrent and an operation log are required.

FIG. 10 illustrates an example of the scenario application plan databaseT7. In the scenario application plan database T7, the content of acandidate (integrated scenario candidate) that is used when a newsimulation function is added to an existing digital twin is indicated.Hereinafter, two candidates are described, but three or more candidatesmay be created.

The scenario application plan database T7 includes a plan name (Plan inFIG. 10 ) C71, an effect on existing configuration C72, an effectsimulation C73, and resource consumption C74, for example.

The plan name C71 is a name of a plan of a case where a new scenario isapplied to an existing digital twin. A digital twin obtained by adding afunction defined by a new scenario to an existing digital twin may becalled an update digital twin. An update digital twin is formed byintegrating multiple scenarios.

The effect on existing configuration C72 indicates an effect which isexerted on the configuration of an existing digital twin. In the effecton existing configuration C72, information indicating which part in theconfiguration of an existing digital twin is changed is recorded. Forexample, the information is “changing setting of a pre-process of acurrent sensor,” “changing a logic for estimating a rotational speed,”or “changing a logic for estimating a torque,” for example.

The effect simulation C73 indicates a simulation result of an effect tobe exerted on the performance of an update digital twin when a newscenario is applied to an existing digital twin. In the digital twinmanagement system 100, candidates of an update digital twin aresimulated with use of data collected in the past, whereby an effect tobe exerted on the performance of the update digital twin is calculated.In the effect simulation C73, such predictions that “the accuracy ofestimating a remaining motor life is lowered by 2%” and “a process timefor estimating a remaining motor life is 60 seconds” are stored.

The resource consumption C74 indicates resources which are consumed bythe corresponding update digital twin. The resources are computerresources including a processor use rate, memory consumption, and theuse rate in the auxiliary storage device, for example.

FIG. 11 illustrates an example of the scenario instance database T8. Thescenario instance database T8 includes a scenario instance ID C81, anapplication digital twin C82, an application scenario template C83, anapplication plan C84, and an application scenario C85, for example. Thescenario instance ID C81 indicates information for identifying eachscenario instance. The application digital twin C82 indicatesidentification information (ID) regarding a digital twin to be applied.The application scenario template C83 indicates a scenario template tobe applied. The application plan C84 indicates a plan (candidate of anupdate digital twin) to be applied. The application scenario C85indicates a scenario to be applied. In the application scenario C85,identification information (ID) about a generated integrated scenarioillustrated in FIG. 15 or 16 is stored. FIG. 11 illustrates an examplein which an integrated scenario B is selected.

A user selects, as “application,” one of plans that are proposed on thescreen to the user. As a result, a digital twin (C82), a scenariotemplate on which the digital twin is based (C83), a plan applied to thedigital twin (C84), and a final process flow (C85) therefor areassociated with one another and managed in the scenario instancedatabase T8.

With reference to a flowchart in FIG. 12 , a digital twin managementprocess will be explained. When an instruction to add a new scenariotemplate to an existing digital twin is received (S11), the digital twinmanagement system 100 determines whether or not data necessary for thenew scenario template is obtainable from an industrial apparatus 300that is a target of the digital twin (S12). Either data obtained from anactual sensor or data obtained from a virtual sensor can be used as thenecessary data.

When determining that the necessary data is not obtainable (S12: NO),the digital twin management system 100 issues an error notification to auser (e.g., system manager) who is using the digital twin managementsystem 100 (S13).

When determining that the necessary data is obtainable (S12: YES), thedigital twin management system 100 compares the configuration of theexisting digital twin with the configuration of the new scenariotemplate, and extracts multiple common layer candidates which are commonto the existing digital twin and the new scenario template (S14).

The digital twin management system 100 creates multiple candidates of anupdate digital twin from the multiple common layer candidates (S15). Thedigital twin management system 100 checks whether there is any past datathat can be used to evaluate each of the update digital twin candidates(S16).

When determining that there is past data that can be used (S16: YES),the digital twin management system 100 executes each of the updatedigital twin candidates (application scenarios) by using the past data,makes respective evaluations by comparison of the obtained result valueswith a past result value (S17), organizes the evaluation results inaccordance with the grouping policy, and narrows down the update digitaltwin candidates in order of high evaluation (S18).

When determining that there is no past data that can be used to evaluatethe update digital twin candidates (S16: NO), the digital twinmanagement system 100 proceeds to step S18 by skipping the evaluationstep (S17).

By displaying, on the monitor display, the description of the updatedigital twin candidates and the evaluations thereof, the digital twinmanagement system 100 presents the information to the user (S19). Theuser selects any one of the update digital twin candidates by using thepresented information, and informs the digital twin management system100 of the selected update digital twin candidate. The digital twinmanagement system 100 changes the configuration of the digital twin insuch a manner as to achieve the update digital twin selected by the user(S20). Further, tables related to the digital twin are updated in linewith the configuration change. Specifically, the digital twin (C82), ascenario template on which the digital twin is based (C83), a planapplied to the digital twin (C84), and an application scenario as afinal process flow (C85) therefor are associated with one another andmanaged in the scenario instance database T8. In addition, theconfigured scenario instance C26 in the digital twin configuration DB T2is updated to the added or updated scenario instance ID C81. Moreover,the identifier (DID C51) in the data store management DB T5 regardinginformation concerning a storage destination of data that is calculatedby application of analysis models that are written in the analysis modelDB T3 for implementing processes of the applied scenario is stored inthe monitoring information C25 in the digital twin configuration DB T2.

With reference to FIGS. 13 to 16 , creation of candidates of an updatedigital twin will be explained. FIG. 13 illustrates a templated processflow that is included in a scenario for estimating the remaining life ofa motor 310. FIG. 14 is a templated process flow that is included in ascenario for estimating the remaining life of a drill 320.

It is assumed that an existing digital twin is configured to estimatethe remaining life of a motor 310 in accordance with the process flowillustrated in FIG. 13 . An example of creating a new integrated digitaltwin candidate by adding a scenario for estimating the remaining life ofa drill 320 illustrated in FIG. 14 to the existing digital twin will beexplained.

In a process flow of the remaining motor life estimation scenario whichis illustrated in FIG. 13 , data (RS-01) obtained from a current sensoris first prepared (S31). An analysis model (NM1) is applied to theprepared sensor data (RS-01) to level the sensor data, so thatpre-processed data (VS-01) is generated (S32). The pre-processed data(VS-01) is inputted to each of analysis models (logics) (S33 to S35).The analysis models include a logic (NM2, S33) of estimating and storingan axis voltage, a logic (NM3, S34) for estimating and storing a torque,and a logic (NM4, S35) for estimating and storing a rotational speed.

Next, in the process flow, data sets (the torque (VS-02), the rotationalspeed (VS-03), the axis voltage (VS-04)) to be inputted to a finalanalysis model are prepared (S36). The prepared data sets are inputtedto an analysis model (NM5) for diagnosing a sign of a motor bearingfailure (S37). A sign of a bearing failure in the motor 310 is diagnosedby the analysis model, the remaining life (VS-05) of the motor 310 isestimated from the failure sign, and the result obtained by the analysismodel is stored in the data store (D-006) (S37).

In a process flow of the remaining drill life estimation scenario whichis illustrated in FIG. 14 , data (RS-01) obtained from a current sensoris prepared (S41). An analysis model (NM6) is applied to the preparedsensor data (RS-01) to level the sensor data, so that pre-processed data(VS-06) is generated (S42). The pre-processed data (VS-06) is inputtedto each of analysis models (logics) (S43, S44). The analysis modelsinclude a logic (NM7, S43) for estimating and storing a torque and alogic (NM8, S44) for estimating and storing a rotational speed.

Next, in the process flow, data sets (the torque (VS-07), the rotationalspeed (VS-08), the material quality (M-01), and the usage history(M-02)) to be inputted to a final analysis model are prepared (S45). Thematerial quality and the usage history refer to the material quality ofthe drill 320 and the usage history of the drill 320, respectively.

The prepared data sets are inputted to an analysis model (NM9) forestimating the remaining drill life (S46), and the result obtained bythe analysis model is stored in a data store (D-008).

An example of a candidate of an update digital twin in a case where theremaining drill life estimation scenario which has been explained withreference to FIG. 14 is added to the digital twin for estimating aremaining motor life which has been explained with reference to FIG. 13will be explained.

FIG. 15 illustrates one example (Plan A, integrated scenario A) of anupdate digital twin candidate. FIG. 15 illustrates a process flow S50Aof a common layer candidate 1, a process flow S60A for estimating aremaining motor life in which the process flow S50A of the common layercandidate 1 is adopted, and a process flow S70A for estimating aremaining drill life in which the process flow S50A of the common layercandidate 1 is similarly adopted.

In this candidate example, steps S31 and S41 of preparing data from acurrent sensor and steps S32 and S42 of pre-processing the data obtainedfrom the current sensor are used in common as step S51 of preparing datafrom a current sensor and step S52 of pre-processing the data obtainedfrom the current sensor, respectively.

FIG. 16 illustrates another example of an update digital twin candidate(Plan B, integrated scenario B). FIG. 16 illustrates a process flow S50Bof a common layer candidate 2, a process flow S60B for estimating aremaining motor life in which the process flow S50B of the common layercandidate 2 is adopted, and a process flow S70B for estimating aremaining drill life in which the process flow S50B of the common layercandidate 2 is similarly adopted.

In this candidate example, not only step S51 of preparing data from acurrent sensor and step S52 of pre-processing the data from the currentsensor but also step S53 of estimating a motor torque by an analysismodel and step S54 of estimating a motor rotational speed by an analysismodel are used in common. Step S53 corresponds to step S34 and step S43.Step S54 corresponds to step S35 and step S44. The version of a torqueestimation analysis program which is used by the torque estimationanalysis model NM3 in step S34 is different from that of a torqueestimation analysis program which is used by the torque estimationanalysis model NM7 in step S43. Thus, to use in common torque estimationsteps in step S53, which one of the programs is to be used needs to bedecided. As one decision method therefor, a method of deciding to use aprogram of a newer version is adopted in the present embodiment.However, the method is not limited to this.

Since the part to be used in common in the candidate example (Plan B,integrated scenario B) illustrated in FIG. 16 is larger than that in thecandidate example (Plan A, integrated scenario A) illustrated in FIG. 15, the sharing rate of computer resources in the Plan B is high and theresource consumption C74 in the Plan B is low, as illustrated in FIG. 10, for example. However, since the part used in common in the candidateexample (Plan B, integrated scenario B) illustrated in FIG. 16 is largerthan that in the candidate example (Plan A, integrated scenario A)illustrated in FIG. 15 , the effect on the existing configuration C72 ofthe Plan B is greater (more items are written in the effect on theexisting configuration C72). Consequently, in many steps, an analysismodel that is different from an analysis model having been used so faris applied, for example, so that there is a possibility that theestimation accuracy and the length of estimation process time in theanalysis are affected. Regarding an effect caused by such a change,integrated scenarios (integrated scenario A, integrated scenario B) areimplemented with use of the past data in step S17, a remaining motorlife estimation value and a length of process time therefor arecalculated, and the calculated values are compared with a resultobtained by a previously applied scenario, and stored in the effectsimulation C73. Through the effect simulation C73, a manager is able tocheck the degree of the effect.

FIG. 17 illustrates an example of a screen G2 on which candidates of anupdate digital twin are presented to a user. The screen G2 is onedetailed example of the screen G1 illustrated in FIG. 1 . On the screenG2, a result of a verification of a digital twin configuration changewhen an existing digital twin is expanded to a new scenario is providedto a user.

The screen G2 includes a general description of update GP21, an updateplan GP22, an impact simulation result GP23, an update button, and acancel button, for example.

The general description of update GP21 indicates a general descriptionof an update of a digital twin. The general description of an updateincludes information for identifying an industrial apparatus 300corresponding to a digital twin, the number of identified industrialapparatuses 300, and information for designating a new scenario to beadded to an existing digital twin, for example.

The update plan GP22 includes information regarding the configuration ofa candidate plan (plan). The information regarding a candidate planincludes information regarding a common layer part, informationregarding a part for estimating a remaining motor life, and a part forestimating a remaining drill life, for example. In the update plan GP22,buttons or tabs for displaying the names, i.e., “Plan A” and “Plan B,”of the candidate plans may be provided in such a way that a user canselect any one of the buttons or tabs. In the update plan GP22, thecontent of a candidate plan selected by a user is displayed.

In the impact simulation result GP23, a result obtained by evaluatingany one of the candidates with use of a predetermined evaluation indexis displayed.

FIG. 18 illustrates one example of the impact simulation result GP23.The impact simulation result GP23 includes a candidate name GP231, aneffect on existing configuration of digital twin (configuration ofprocess flow) GP232, an effect simulation GP233, computer resourceconsumption GP234, and an update time GP235, for example.

In the effect on existing configuration of digital twin GP232, a settingchange in a pre-process of sensor data and a change of an analysis modelare displayed, for example. In the effect simulation GP233, a simulationresult regarding a performance change when a digital twin is updated inaccordance with a selected plan is displayed. A process of the effectsimulation is performed using past data. The effect simulation indicatesa percentage by which the remaining motor life estimating accuracy isincreased (or reduced), and a change in a length of time required forestimating a remaining motor life, for example.

The computer resource consumption GP234 indicates consumption of varioustypes of resources such as a processor usage rate, memory consumption,consumption in the auxiliary storage device, for example. In addition,the sharing rate of computer resources is also indicated in the computerresource consumption.

The update time GP235 indicates a length of time required for updating adigital twin in accordance with a selected plan, and whether reboot isrequired or not.

According to the present embodiment configured as described above, in acase where a new scenario is added to a digital twin that is beingexecuted, information regarding candidates of an update digital twin canbe provided to a user. Consequently, the user-friendliness is improved.

According to the present embodiment, multiple candidates theconfigurations of which are partially used in common and a result of asimulation in which each of the candidates is selected can be providedto a user. Consequently, the user can objectively and efficientlydetermine which candidate is preferable.

Second Embodiment

The second embodiment will be explained with reference to FIG. 19 . Inthe present embodiment, the difference from the first embodiment willmainly be explained. FIG. 19 is a flowchart of a digital twin managementprocess.

To update a digital twin by adding a new scenario thereto (S11 to S20),the digital twin management system 100 of the present embodimentcalculates a possible effect on a separate system 500 or an industrialapparatus 300 corresponding to the digital twin (S21).

In reference to the result of the calculation in step S21, the digitaltwin management system 100 determines whether issuance of a notificationto the industrial apparatus 300 or the separate system 500 is needed(S22). When it is determined that such a notification is not needed(S22: NO), the present process is ended.

When determining that a notification to the industrial apparatus 300 orthe separate system 500 is needed (S22: YES), the digital twinmanagement system 100 issues a notification indicating that the digitaltwin has been updated, to the industrial apparatus 300 or the separatesystem 500 (S23).

The abovementioned present embodiment also provides the same effect asthose of the first embodiment. According to the present embodiment,further, a digital twin is updated, so that, if the accuracy ofestimating the remaining lives of main components of industrialapparatuses 300 is degraded, for example, the inventories of the maincomponents can be increased, or a timing for ordering the maincomponents can be set earlier. Consequently, the user-friendliness isfurther improved.

It is to be noted that the present invention is not limited to theabovementioned embodiments, and encompasses a variety of modifications.For example, since the abovementioned embodiments have been provided toexplain the present invention in detail in an easy-to-understand manner,the present invention is not necessarily limited to an embodimentincluding all the components explained herein. Moreover, a part of oneof the embodiments can be replaced with a part of the other embodiment.Further, a part of one of the embodiments can be added to the otherembodiment. In addition, a part of the embodiments can be omitted, bereplaced with another part, or be added to another part.

For example, the performance of a main component such as a controlsubstrate or a power source circuit in an industrial apparatus can beevaluated, and the remaining life thereof can be estimated.

What is claimed is:
 1. A digital twin management system for managing adigital twin that represents an actual physical system in a virtualspace, the digital twin management system comprising: a processor; and amemory that stores a predetermined computer program that is executed bythe processor, wherein, to generate an integrated scenario by adding asecond scenario to a first scenario, the processor extracts multipleparts that can be used in common in the first scenario and the secondscenario, generates multiple integrated scenario candidates that arecandidates of the integrated scenario, by changing the extracted partsthat can be used in common, calculates an evaluation of each of thegenerated integrated scenario candidates, and outputs configurationinformation regarding each of the integrated scenario candidates and theevaluation of the integrated scenario candidate in association with eachother.
 2. The digital twin management system according to claim 1,wherein each of the first scenario, the second scenario, and theintegrated scenario is formed by templating, as a scenario, a digitaltwin configuration that includes information indicating a configurationof a target actual system, attribute information indicating a purpose ofthe scenario, data derived from the actual system, a process flow thatis executed in reference to the obtained data, an analysis modelincluded in the process flow, and an input/output parameter of theanalysis model.
 3. The digital twin management system according to claim2, wherein, by comparison of the first scenario with the secondscenario, multiple parts that can be used in common by the firstscenario and the second scenario are extracted.
 4. The digital twinmanagement system according to claim 1, wherein configurationinformation regarding each of the integrated scenario candidates and theevaluation of the integrated scenario candidate are provided to a userthrough a monitor display.
 5. The digital twin management systemaccording to claim 4, wherein the evaluation is given in terms ofresource consumption and/or performance stability.
 6. The digital twinmanagement system according to claim 5, wherein past data that has beenused in the first scenario is used to calculate the evaluation.
 7. Thedigital twin management system according to claim 6, wherein selectionfrom among the integrated scenario candidates is made in accordance witha prepared policy.
 8. The digital twin management system according toclaim 4, wherein the evaluation includes resource consumption,performance stability, and a possible effect on a separated systemrelated to the actual system.
 9. The digital twin management systemaccording to claim 8, wherein, regarding an integrated scenariocandidate selected by a user from among the integrated scenariocandidates, information regarding a possible effect on the separatesystem is provided to the separate system and/or another system.
 10. Thedigital twin management system according to claim 1, wherein the actualsystem is an industrial apparatus that is used in a factory.
 11. Adigital twin management method that is a virtual model management methodfor managing a digital twin that represents an actual physical system ina virtual space by using a computer, wherein, to add a second scenarioto a first scenario, the computer extracts multiple parts that can beused in common in the first scenario and the second scenario, generatesmultiple candidates of an integrated scenario by changing the extractedparts that can be used in common, calculates an evaluation of each ofthe generated integrated scenario candidates, and outputs configurationinformation regarding each of the integrated scenario candidates and theevaluation of the integrated scenario candidate in association with eachother.